Fault tree analysis is a commonly used technique to assess the systems reliability
performance in terms of its components reliability characteristics. More recently, the
Binary Decision Diagram (BDD) methodology has been introduced which significantly
aids the analysis of the fault tree diagram. The approach has been shown to improve
both the efficiency of determining the minimal cut sets of the fault tree, and also the
accuracy of the calculation procedure used to quantify the top event parameters.
To utilise the technique the fault tree structure needs to be converted into the BDD
format. Converting the fault tree is relatively straightforward but requires the basic
events of the tree to be placed in an ordering. The ordering of the basic events is
critical to the resulting size of the BDD, and ultimately affects the performance and
benefits of this technique.
Numerous studies have tackled this variable ordering problem and a number of
heuristic approaches have been developed to produce an optimal ordering permutation
for a specific tree. These heuristic approaches do not always yield a minimal BDD
structure for all trees, some approaches generate orderings that are better for some trees
but worse for others. The most recent research to find an approach to produce an
optimal ordering for a range of trees has looked at pattern recognition approaches, such
as genetic algorithm based classifier systems.
This paper reviews the heuristic approaches that have been established and examines
the pattern recognition techniques that have been applied more recently. Another
potential new algorithm for ordering using the structural importance of the components
is proposed.